To solve the interference of smoke environment on infrared image enhancement processing and highlight the contour details of the target, an infrared smoke image enhancement method based on guided filter image layering is proposed. Firstly, the image is divided into base layer and detail layer by guided filtering, and the detail layer is enhanced by fractional differential mask. Then, based on the characteristics of infrared smoke image, a secondary stratification method is designed. The base layer is divided into original layer and contour layer by anisotropic diffusion. Then adaptive histogram equalization is performed on the original layer, and the contour layer is amplified and merged with the detail layer. Finally, the average brightness is used to set the weight function, and the two layers of images are weighted to obtain the enhanced image. The experimental results show that compared with other enhancement algorithms, the proposed method can more effectively improve the clarity of infrared images under smoke and dust interference and highlight their detailed texture features. The average gradient and information entropy of the three groups of images after enhancement are 7.7211 and 5.8114, which are 1.0119 and 3.1778 higher than the original images. To solve the interference of smoke environment on infrared image enhancement processing and highlight the contour details of the target, an infrared smoke image enhancement method based on guided filter image layering is proposed. Firstly, the image is divided into base layer and detail layer by guided filtering, and the detail layer is enhanced by fractional differential mask. Then, based on the characteristics of infrared smoke image, a secondary stratification method is designed. The base layer is divided into original layer and contour layer by anisotropic diffusion. Then adaptive histogram equalization is performed on the original layer, and the contour layer is amplified and merged with the detail layer. Finally, the average brightness is used to set the weight function, and the two layers of images are weighted to obtain the enhanced image. The experimental results show that compared with other enhancement algorithms, the proposed method can more effectively improve the clarity of infrared images under smoke and dust interference and highlight their detailed texture features. The average gradient and information entropy of the three groups of images after enhancement are 7.7211 and 5.8114, which are 1.0119 and 3.1778 higher than the original images. To solve the interference of smoke environment on infrared image enhancement processing and highlight the contour details of the target, an infrared smoke image enhancement method based on guided filter image layering is proposed. Firstly, the image is divided into base layer and detail layer by guided filtering, and the detail layer is enhanced by fractional differential mask. Then, based on the characteristics of infrared smoke image, a secondary stratification method is designed. The base layer is divided into original layer and contour layer by anisotropic diffusion. Then adaptive histogram equalization is performed on the original layer, and the contour layer is amplified and merged with the detail layer. Finally, the average brightness is used to set the weight function, and the two layers of images are weighted to obtain the enhanced image. The experimental results show that compared with other enhancement algorithms, the proposed method can more effectively improve the clarity of infrared images under smoke and dust interference and highlight their detailed texture features. The average gradient and information entropy of the three groups of images after enhancement are 7.7211 and 5.8114, which are 1.0119 and 3.1778 higher than the original images.